Authors
Abstract
Introduction
Agricultural production is affected by risks originated from weather and international markets. Although these risks could never completely been removed, we can minimize their effects by realizing the effective parameters in plant growth and crop yield and consequently by estimating the crop yield amount. Among these parameters, climate has a more significant role, especially in rainfed crops. Rainfed wheat is one of the major agricultural crops in Kurdistan province that includes most of the cultivated area. In 2006, Kurdistan province had %11.8 of the cultivated area which encompasses %13.67 of the rainfed wheat yield of the country. Regarding environmental outcomes, quite good prediction may be acquired by empirical fits of these crop-yield weather regression type models to real datasets. The aim of this paper is achieving higher accuracy revealed statistical models for rainfed wheat yield in different plant growth stages, regarding weather parameters and some specific agrometeorological indices. It is noticeable that non-weather parameters such as economic and management consideration to rainfed wheat yield were not considered in this study.
Methodology
Therefore, in this study the prediction of rainfed wheat yield in Kurdistan province has been carried out, based on agrometeorological indices and climatological parameters. For this purpose Ranifed wheat yield data for Kurdistan province (34: 44? to 36:30? N? to 45:31? to 48:16?E) as well as its counties include Bijar, Sanandaj, Saghez, Ghorveh, Marivan and Divandareh, were obtained from Iran Aagriculture Ministry and also necessary weather parameters were obtained for all the weather stations in Kurdistan province from Iranian National Meteorological Organization for the period 1991-2006 (1993-2006 for Marivan station). Correlation and nearest neighboring methods were used for filling the missing data. Then linear stepwise regression models were developed for rainfed wheat yield data and independent parameters during 1991- 2003 years (1993-2003 for Marivan station). Stepwise regression method was chosen due to high amount of the independent parameters. The independent parameters in this study are 5 agrometeorological indices include; Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units (PTU), Vapor pressure deficit (VPD), Temperature Differences (TD) and 12 climatological parameters include; average maximum (Tmax) and minimum temperature (Tmin), absolute maximum (Tabs(max)) and minimum temperature (Tabs(min)), average (FF) and absolute (FFabs(max)) wind speed, relative humidity (RH), total (PET(total)) and average evapotranspiration (PET), sunshine hours (SH), total precipitation (R), rainy days (R(day)). Each daily amount of hese parameters has been extracted for six phenological phases of plant growing season from sowing to harvest. These stages are; the first stage of active vegetative before dormancy stage from November 7th to December 11th, dormancy stage from December 12th to March 15th, the second stage of active vegetative after dormancy stage from March 16th to May 10th, reproductive stage from May 11th to June 9th and maturity stage from June 10thto July 10th. In order to obtain the best models, regression models were calibrated for each rainfed wheat yield stage as well as the entire growing Season and that of the start of second stage of active vegetative after dormancy stage to the end of reproductive stage from March 16th to June 9th. Thus, 8 regression models were calculated for each study area.
After entering The independent parameters in stepwise regression models the predictive parameters were chosen for each station and each phenological phase and based on R, R2, and SEOE the best models were chosen. Then crop yield for 2003-2006 is estimated, accordingly.
Results and Discussion
The developed models show that 81, 70.2, 82.2, 71, 80, 90.6 and 65.6 percent of wheat yield variations is due to climatological parameters and agrometeorological indices for Baneh, Marivan, Divandareh, Bijar, Ghorveh, Saghez and Sanandaj provinces, respectively. In addition, the best phenological phase for predicting wheat yield for Bijar, Ghorveh, Saghez provinces are reproductive stage(May 11th to June 9th), for Baneh province is the second stage of active vegetative after dormancy phase(March 16th to May10th) and for Marivan is the dormancy phase (December 12th to March15th). For Sanandaj and Divandare district regression models are developed by using the data of all the growing season.
Conclusion
Based on developed regression models for Kurdistan provinde in this study and the comparesion between these models and previous studies, it is obvious that with a combination of climatological parameters and agrometeorological indices and using stepwise regression models can predict higher amounts of rainfed wheat yield variation.
Keywords
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